CN112488181B - Service fault high-response matching method based on MIDS-Tree - Google Patents
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Abstract
The invention provides a service fault high-response matching method based on an MIDS-Tree. Selecting and denoising a multi-element fusion data set; performing label processing facing to specific service faults and degradation types to obtain a multi-metadata set; classifying the fault types of the data sets to form a multi-element micro-service fault data set; extracting characteristics and attributes oriented to the multi-type service faults by an Apriori method; performing characteristic attribute sensitivity calculation according to the extracted characteristic attributes to obtain optimal characteristic attributes, performing situation analysis, and obtaining a fluctuation range of the service fault characteristic attributes; and establishing the MIDS-Tree and predicting the service fault according to the situation analysis and the service fault type information, so that the resource utilization rate of the service is maximized. The lightweight architecture model provided by the invention aims to solve the problems that the existing service fault prediction method has complicated and redundant models; meanwhile, the model can quickly and highly respond to service-oriented fault strategy matching, and the response time of the existing model is prolonged.
Description
Technical Field
The invention belongs to the field of micro-services; in particular to a service failure high-response matching method based on MIDS-Tree.
Background
The microservice is an important mode and a typical technology of the current software system architecture, has the characteristics of light weight, fast iteration, cross-platform and the like, can enable the deployment, management and maintenance of the system to be faster and more convenient, and gradually becomes the development trend of the system architecture technology. However, due to the technical diversity and distributed complexity, service dependence, interaction invocation and the like are performed throughout the whole process, and service failure cannot be avoided, and even the performance of the whole system is affected, especially under the background of resource limitation and high load. Service faults have the characteristics of changeability, complexity, uncertainty and the like, common fault processing methods comprise technologies such as degradation, isolation, current limiting and the like, services are correspondingly processed by setting a certain time threshold or failure calling times or according to different strategies such as priority and the like, but the method is often a processing mode after the service faults and cannot be predicted and processed in advance according to more reasonable strategies, the service quality and the resource utilization rate are reduced to a certain extent, and how to quickly and efficiently identify, process and recover the services is of great importance.
The failure prediction technology is one of the main methods because it can ensure the effectiveness and reliability of the service and high resource utilization rate, and it is an important way to perform micro-service failure prediction by analyzing historical log data. Data generated in the service operation process has discreteness and diversity, corresponding relation exists between the data and service faults, and the method for effectively mining the incidence relation between the fault data set and various faults through data mining and the like has higher feasibility.
Disclosure of Invention
The invention provides a service fault high-response matching method based on an MIDS-Tree, which is a lightweight high-response architecture model and aims to solve the complex problems of complexity, redundancy and the like of the model in the past in the existing service fault prediction method model; meanwhile, the lightweight model can quickly and highly respond to service-oriented fault strategy matching, and the problem of low response time of the existing model is solved. The problems of processing time and service quality requirements of a software system on service faults and the like under the background of resource limitation and high load concurrency are better met.
The invention is realized by the following technical scheme:
a service failure high-response matching method based on MIDS-Tree comprises the following steps:
step 1: performing multivariate data attribute selection and denoising treatment facing to microservice faults on a multivariate microservice data set intelligently converged by a microservice monitoring platform;
and 2, step: performing service fault-oriented label processing on the data subjected to the multi-element data attribute selection and denoising processing in the step 1 to obtain a denoising highly-available label-containing multi-element microservice fault data set;
and step 3: performing service fault type classification based on labels on the multivariate microservice fault data set in the step 2 to form a multivariate microservice fault data set facing to a service fault type;
and 4, step 4: extracting the characteristic attributes of the Apriori algorithm facing the multi-type service faults from the classified multivariate microservice fault data set in the step 3, mining the multivariate microservice fault data set through the Apriori algorithm and the self-set minimum support degree and confidence coefficient, and finally obtaining the relevance between each type of fault of microservice and the sample attribute characteristics;
and 5: carrying out sensitivity calculation facing to specific type faults and degradation characteristic attributes on the characteristic attributes extracted from the multivariate microservice fault data set in the step 4, and finally screening out optimal characteristic attributes according to the extracted characteristic attributes and the sensitivities of the characteristic attributes;
and 6: analyzing the situation of the service fault facing the relevant type through the optimal characteristic attribute in the step 5 to obtain a service fault fluctuation range facing the characteristic attribute;
and 7: establishing a multi-type fault isolation or degradation MIDS-Tree based on the situation analysis of the optimal characteristic attributes in the step 6 and the characteristic attributes and the service fault degradation strategy;
and 8: and (4) performing service fault degradation prediction on the target service according to the MIDS-Tree established in the step (7), and performing strategy matching of degradation or fault isolation so as to maximize the resource utilization rate.
Further, the step 1 specifically comprises: the method comprises the steps of collecting service load data, CPU utilization rate, failure number, time delay or access amount multi-element micro-service monitoring data in a micro-service operation monitoring process for a micro-service monitoring platform, carrying out data selection facing fault isolation and degradation and data preprocessing facing noise removal, and finally obtaining a low-noise high-availability multi-element micro-service data set facing fault isolation and degradation.
Further, step 2 is specifically to perform label processing on the preprocessed service fault isolation and degradation-oriented multi-metadata set, so as to distinguish a specific service fault reflected by each data record in the data set.
Further, step 3 specifically includes performing service fault type classification facing multivariate microservice load fault isolation and degraded tag data sets on the multivariate service fault data after the tag processing.
Further, the association rule in the Apriori-based feature attribute extraction in step 4 is specifically that a — > B should satisfy: a and B are proper subsets of a classified multivariate microservice fault data set D, the A and B have no intersection, and the A is a condition that a service fault data set sample attribute is the attribute B of the multivariate service fault data set sample; and the support degree and the confidence degree are customized, wherein the support degree indicates whether the rule is significant in all transactions, namely the support degree is higher, and the association rule is more important.
The confidence and support calculations are performed by the following formulas:
confidence coefficient: s (a → B) = P (AB);
the support degree is as follows: c (a → B) = P (B | a).
Further, the step 4 further comprises the following steps,
step 4.1: firstly, scanning a multi-element service fault data set; the first scan yields a frequent 1-item set of sample attributes forming a set D 1 Sequentially circulating until the set is empty and stopping; wherein the multiple service fault data set of the nth scanning is a result set D of the (n-1) th scanning n-1 Further, the candidate set D of this time is generated n (ii) a Wherein n is>1；
Step 4.2: determining the support of the candidate set in the process of step 4.1;
step 4.3: and finally, excavating an association rule set facing the multi-type service faults of the 1-frequent item set according to the confidence coefficient and the support degree.
Further, the step 5 is specifically to generate a 1-frequent item set association rule set for the attribute features of the multi-type service fault sample according to the self-set minimum support and confidence, and perform sensitivity calculation for the attribute features of the specific type fault and degradation according to the 1-frequent item set, where a sensitivity formula for calculating the sample attributes is as follows:
wherein, p (x) i ) Representing random events x i The probability of (c).
Further, the step 6 specifically includes performing situation analysis of the service fault according to the optimal 1-frequent item set association rule, then sorting the data values of the characteristic attribute, and finally obtaining a fluctuation range of the service fault sample attribute as follows: [ V ] min ,V max ]In which V is min ,V max The minimum and maximum values of the sample property are respectively.
Further, the step 7 is specifically to establish the service failure policy number according to the characteristic attribute of each service failure, and the structure thereof should be, from top to bottom: the root node is the input service sample attribute value; one layer of nodes are service fault characteristic attributes; fluctuation range of the characteristic attribute of the two-layer node; the three-layer nodes are the specific service fault types; and the four-layer node services a fault isolation degradation strategy.
The beneficial effects of the invention are:
1. the invention associates and matches the characteristic attribute historical experience value analysis result with the service fault and the fault processing strategy to form the MIDS-Tree, can quickly carry out high-response matching and prediction, and reduces the service fault processing time so as to achieve the maximization of improving the service quality condition and the service resource utilization rate.
2. Compared with the existing service fault positioning and predicting method, the method has the advantages of high response, light weight structure, convenience in calculation and the like; compared with the existing Apriori algorithm, the method has the advantages that the information entropy calculation is introduced, so that the method has more accurate capability of excavating characteristic attributes and comprehensive judgment of multiple angles; compared with the traditional fault Tree establishment, the MDIS-Tree has a high-definition structure, and compared with the traditional fault Tree, the MDIS-Tree is improved to introduce a service fault response strategy, so that the improved fault Tree becomes a more comprehensive fault processing Tree.
Drawings
FIG. 1 is a composite architecture diagram of the present invention.
FIG. 2 is a flow chart of feature attribute extraction based on Apriori-information entropy in accordance with the present invention.
FIG. 3 is a MIDS-Tree Tree architecture diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The method mainly comprises the steps of constructing the MIDS-Tree to carry out high-response matching on service faults, adopting an Apriori-information entropy model, rapidly excavating a frequent item set reflecting the service faults and degradation of the type through an Apriori algorithm, carrying out quantitative processing on characteristic attributes in the frequent item set by using the information entropy model, matching the range after characteristic attribute situation analysis with the service fault types, and formulating corresponding processing strategies according to corresponding service faults, so that the service faults can be effectively processed in time after the service faults are predicted, and high computing capacity, high availability and high resource utilization rate of a platform are further guaranteed.
A service failure high-response matching method based on MIDS-Tree comprises the following steps:
step 1: performing multivariate data attribute selection and denoising treatment facing to microservice faults on a multivariate microservice data set intelligently converged by a microservice monitoring platform;
and 2, step: performing service fault-oriented label processing on the data subjected to the multi-element data attribute selection and denoising processing in the step 1 to obtain a denoising highly-available label-containing multi-element microservice fault data set;
and step 3: performing service fault type classification based on labels on the multivariate microservice fault data set in the step 2 to form a multivariate microservice fault data set facing to service fault types;
and 4, step 4: extracting the characteristic attributes of the Apriori algorithm facing the multi-type service faults from the classified multivariate microservice fault data set in the step 3, and mining the multivariate microservice fault data set through the Apriori algorithm and the self-set minimum support degree and confidence coefficient to finally obtain the relevance between each type of fault of microservice and the sample attribute characteristics;
and 5: carrying out sensitivity calculation facing to specific type faults and degradation characteristic attributes on the characteristic attributes extracted from the multivariate microservice fault data set in the step 4, and finally screening out optimal characteristic attributes according to the extracted characteristic attributes and the sensitivities of the characteristic attributes;
step 6: analyzing the situation of the service fault facing the relevant type through the optimal characteristic attribute in the step 5 to obtain a service fault fluctuation range facing the characteristic attribute;
and 7: establishing a multi-type fault isolation or degradation MIDS-Tree based on the situation analysis of the optimal characteristic attribute in the step 6 and the characteristic attribute and service fault degradation strategy;
and 8: and (4) performing service fault degradation prediction on the target service according to the MIDS-Tree established in the step (7), and performing strategy matching of degradation or fault isolation so as to maximize the resource utilization rate.
Further, the step 1 specifically comprises: the method comprises the following steps of collecting service load data in a micro-service operation monitoring process for a micro-service monitoring platform, for example: the method comprises the steps of monitoring data of the multi-element micro-service such as the CPU utilization rate, the failure number, the time delay, the access amount and the like, carrying out data preprocessing such as data selection and denoising facing fault isolation and degradation, and finally obtaining a low-noise high-availability multi-element micro-service data set facing fault isolation and degradation.
Further, step 2 is specifically to perform label processing on the preprocessed service fault isolation and degradation-oriented multi-metadata set, so as to distinguish a specific service fault reflected by each data record in the data set.
Further, the step 3 is specifically to perform service fault type classification facing to the multivariate microservice load fault isolation and degraded tag data set on the multivariate service fault data after the tag processing.
Further, the association rule in the Apriori-based feature attribute extraction in step 4 is specifically that a — > B should satisfy: a and B are true subsets of the classified multivariate microservice fault data set D, the A and the B have no intersection, and the A is the condition that the sample attribute of the service fault data set is the sample attribute B of the multivariate service fault data set; and the support degree and the confidence degree are customized, wherein the support degree indicates whether the rule is significant in all transactions, namely the support degree is higher, and the association rule is more important.
The calculation of confidence and support is performed by the following formula:
confidence coefficient: s (a → B) = P (AB);
the support degree is as follows: c (a → B) = P (B | a).
Further, the step 4 further comprises the following steps,
step 4.1: firstly, scanning a multi-element service fault data set; the first scan yields a frequent 1-item set of sample attributes forming a set D 1 Sequentially circulating until the set is empty and stopping; wherein, the n (n) th>1) The multi-service failure data set of the scanning is the result set D of the n-1 th scanning n-1 Further, the candidate set D of this time is generated n ；
Step 4.2: determining the support of the candidate set in the process of step 4.1;
step 4.3: and finally, excavating an association rule set facing the multi-type service faults of the 1-frequent item set according to the confidence coefficient and the support degree.
Further, the step 5 is specifically to generate a 1-frequent item set association rule set for the attribute features of the multi-type service fault sample according to the self-set minimum support and confidence, and perform sensitivity calculation for the attribute features of the specific type fault and degradation according to the 1-frequent item set, where a sensitivity formula for calculating the sample attributes is as follows:
wherein, p (x) i ) Representing random events x i The probability of (c).
Further, the step 6 is specifically to perform situation analysis of the service fault according to the optimal 1-frequent item set association rule, then sort the data values of the characteristic attribute, and finally obtain a fluctuation range of the service fault sample attribute as follows: [ V ] min ,V max ]In which V is min ,V max The minimum and maximum values of the sample property are respectively.
Further, the step 7 is specifically to establish the service failure policy number according to the characteristic attribute of each service failure, and the structure thereof should be, from top to bottom: the root node is the input service sample attribute value; one layer of nodes are service fault characteristic attributes; the fluctuation range of the characteristic attribute of the second-layer node; the three-layer nodes are the specific service fault types; and the four-layer node services a fault isolation degradation strategy.
Example 2
Assuming the target service failure degradation criteria is 5 seconds and 20 times, under the conventional method, the connection is continued until a degradation condition is triggered, thereby triggering a failure degradation policy. According to the high-response service fault strategy matching method of the MIDS-Tree established according to the invention, firstly, the characteristic attribute of failure degradation is extracted, the failure times are obtained as the characteristic attribute, the situation analysis facing the failure degradation is carried out according to the historical experience value of the failure times, and the connection failure is supposed to occur if the analysis finds that 10-15 connection failures occur within 2 seconds. According to the failure degradation-oriented high-response matching strategy method of the MIDS-Tree established based on the conditions, when a new service is initiated, if the failure times are within 10-15 times within 2 seconds, the failure degradation-oriented strategy matching with high response is carried out quickly, accurately and efficiently according to the established MIDS-Tree, so that the resource utilization rate is maximized and the efficiency is higher.
Example 3
It can be seen from fig. 1 that, by using the method provided by the present invention, feature extraction oriented to specific types of service failures is performed from historical data, situation analysis is performed based on historical experience values of feature attributes, and finally a lightweight, clear-structured and well-arranged MIDS-Tree is established.
Firstly, preprocessing a multi-element microservice data set.
Because a large amount of data is generated in the process of monitoring service operation, wherein some data noises can influence the prediction precision of the invention, the invention firstly selects the relevant service load attributes required by the invention as follows: the method comprises the steps of utilizing a CPU, utilizing a memory, failing times, accessing quantity and time delay, preprocessing related noise data in a selected data set, finally obtaining a low-noise high-availability service load data set, and performing label processing on the denoised data set so as to map corresponding service fault types.
And secondly, classifying the coarse-grained service faults based on the labels.
And utilizing the preprocessed multi-metadata set obtained in the first step to classify the service fault types based on the label types.
And thirdly, extracting features based on Apriori-information entropy.
According to the method shown in FIG. 2, firstly, the multivariate microservice fault data set classified in the second step is utilized to extract the characteristic attribute association rule of the Apriori algorithm facing to the multi-type service fault, and the multivariate service fault data set is scanned for the first time to obtain the characteristic attribute association ruleThe frequent 1-item set of sample attributes forms a set D 1 And sequentially looping to stop until the set is empty. Wherein, the n (n) th>1) The multi-service failure data set of the scanning is the result set D of the n-1 th scanning n-1 Further, the candidate set D of this time is generated n (ii) a The support of the candidate set is then determined in the process. According to the method, a characteristic attribute association rule set facing the multi-type service fault of a 1-frequent item set is mined according to the confidence degree and the support degree. Wherein the support degree shows how representative the rule is in all transactions, obviously, the greater the support degree is, the more important the association rule is. The calculation of confidence and support is performed by the following formula:
S(A→B)＝P(AB)；
C(A→B)＝P(B|A)；
s (a → B) represents support, C (a → B) represents confidence, A (AB) represents probability of two events occurring together, and P (B | a) represents conditional probability.
And finally, generating a 1-frequent item set association rule set facing the multi-type service fault sample attribute characteristics according to the self-set minimum support and confidence, carrying out sensitivity calculation facing the attribute characteristics of specific type faults and degradation according to the set, and calculating the sensitivity of the sample attributes through the following formula:
wherein, p (x) i ) Representing random events x i The probability of (c).
And fourthly, analyzing the situation based on the characteristic attributes.
And performing empirical analysis based on the characteristic attribute historical value by using the attribute characteristics extracted in the third step. Firstly, the maximum sensitivity screened out according to the sensitivity is the optimal characteristic and attribute, the situation analysis of the service fault is carried out according to the optimal characteristic attribute, then the fluctuation range of the characteristic attribute is taken as a value, and finally the fluctuation range of the service fault sample attribute is obtained as follows: [ V ] min ,V max ]In which V is min ,V max Are respectively provided withThe sample property minimum and maximum values.
And fifthly, establishing the MIDS-Tree.
And establishing a correlation tree with the service fault type, the service fault and the degradation strategy by using the range result after the characteristic attribute situation analysis in the fourth step, and quickly predicting the service quality condition so as to maximize the utilization rate of service resources.
According to the illustration in fig. 3, the service failure policy number is established for the characteristic attribute of each service failure, and the structure thereof should be, from top to bottom: the root node is an input service sample attribute value; one layer of nodes are service fault characteristic attributes; the fluctuation range of the characteristic attribute of the second-layer node; the three layers of nodes are the specific service faults: service fault isolation degradation strategy.
As can be seen from FIG. 1, compared with the conventional service fault prediction method, the method of the present invention has the advantages of light weight, high response, low error and clear structure; compared with the Apriori-based feature extraction method, the method introduces the information entropy index to enable the information entropy index to have more comprehensive and more accurate feature extraction results, and reduces the precision problem caused by errors caused by data in the excavation process; compared with the traditional mixed attribute situation analysis method, the situation analysis method is more targeted and more accurate according to historical experience data.
Claims (8)
1. A service failure high-response matching method based on MIDS-Tree is characterized by comprising the following steps:
step 1: performing multivariate data attribute selection and denoising treatment facing to microservice faults on a multivariate microservice data set intelligently converged by a microservice monitoring platform;
and 2, step: performing service fault-oriented label processing on the data subjected to the multi-element data attribute selection and denoising processing in the step 1 to obtain a denoising highly-available label-containing multi-element microservice fault data set;
and 3, step 3: performing service fault type classification based on labels on the multivariate microservice fault data set in the step 2 to form a multivariate microservice fault data set facing to a service fault type;
and 4, step 4: extracting the characteristic attributes of the Apriori algorithm facing the multi-type service faults from the classified multivariate microservice fault data set in the step 3, mining the multivariate microservice fault data set through the Apriori algorithm and the self-set minimum support degree and confidence coefficient, and finally obtaining the relevance between each type of fault of microservice and the sample attribute characteristics;
and 5: carrying out sensitivity calculation facing to specific type faults and degradation characteristic attributes on the characteristic attributes extracted from the multivariate microservice fault data set in the step 4, and finally screening out optimal characteristic attributes according to the extracted characteristic attributes and the sensitivities of the characteristic attributes;
specifically, in step 5, a 1-frequent item set association rule set for the attribute features of the multi-type service fault sample is generated according to the self-set minimum support and confidence, sensitivity calculation for the attribute features of specific types of faults and degradation is performed according to the 1-frequent item set, and a sensitivity formula for calculating the sample attributes is as follows:
wherein, p (x) i ) Representing random events x i The probability of (d);
performing empirical analysis based on the characteristic attribute historical values; firstly, the maximum sensitivity screened out according to the sensitivity is the optimal characteristic and attribute, the situation analysis of the service fault is carried out according to the optimal characteristic attribute, then the fluctuation range of the characteristic attribute is taken as a value, and finally the fluctuation range of the service fault sample attribute is obtained as follows: [ V ] min ,V max ]In which V is min ,V max Respectively the minimum value and the maximum value of the sample attribute;
and 6: analyzing the situation of the service fault facing the relevant type through the optimal characteristic attribute in the step 5 to obtain a service fault fluctuation range facing the characteristic attribute;
and 7: establishing a multi-type fault isolation or degradation MIDS-Tree based on the situation analysis of the optimal characteristic attribute in the step 6 and the characteristic attribute and service fault degradation strategy;
and 8: and (4) performing service fault degradation prediction on the target service according to the MIDS-Tree established in the step (7), and performing strategy matching of degradation or fault isolation so as to maximize the resource utilization rate.
2. The MIDS-Tree based service failure high response matching method according to claim 1, wherein the step 1 specifically comprises: the method comprises the steps of collecting service load data, CPU utilization rate, failure number, time delay or access amount multi-element micro-service monitoring data in a micro-service operation monitoring process for a micro-service monitoring platform, carrying out data selection facing fault isolation and degradation and data preprocessing facing noise removal, and finally obtaining a low-noise high-availability multi-element micro-service data set facing fault isolation and degradation.
3. The MIDS-Tree based service fault high-response matching method according to claim 1, wherein the step 2 is specifically to label the preprocessed service fault isolation and degradation oriented multi-metadata set to distinguish specific service faults reflected by each data record in the data set.
4. The MIDS-Tree based service fault high-response matching method according to claim 1, wherein the step 3 is to perform service fault type classification facing multivariate microservice load fault isolation and degraded tag data sets on the multivariate service fault data after tag processing.
5. The MIDS-Tree-based service fault high-response matching method according to claim 4, wherein the association rule in the Apriori-based feature attribute extraction of the step 4 is specifically that A- > B should satisfy: a and B are true subsets of the classified multivariate microservice fault data set D, the A and the B have no intersection, and the A is the condition that the sample attribute of the service fault data set is the sample attribute B of the multivariate service fault data set; the support degree and the confidence degree are customized, wherein the support degree indicates whether the rule is significant in all transactions, namely the support degree is larger, and the association rule is more important;
the confidence and support calculations are performed by the following formulas:
confidence coefficient: s (a → B) = P (AB);
the support degree is as follows: c (a → B) = P (B | a).
6. The MIDS-Tree based service failure high response matching method according to claim 5, wherein said step 4 further comprises the following steps,
step 4.1: firstly, scanning a multi-element service fault data set; the first scan yields a frequent 1-item set of sample attributes forming a set D 1 Sequentially circulating until the set is empty and stopping; wherein the multivariate service fault data set of the nth scanning is a result set D of the (n-1) th scanning n-1 Further, the candidate set D of this time is generated n (ii) a Wherein n is>1；
And 4.2: determining the support of the candidate set in the process of step 4.1;
step 4.3: and finally, excavating an association rule set facing the multi-type service faults of the 1-frequent item set according to the confidence coefficient and the support degree.
7. The MIDS-Tree based service fault high-response matching method according to claim 1, wherein the step 6 specifically comprises performing service fault situation analysis according to the optimal 1-frequent item set association rule, then sorting the data values of the characteristic attribute, and finally obtaining the fluctuation range of the service fault sample attribute as follows: [ V ] min ,V max ]In which V is min ,V max The minimum and maximum values of the sample property are respectively.
8. The MIDS-Tree-based service fault high-response matching method according to claim 1, wherein the step 7 is specifically to establish the number of service fault strategies according to the characteristic attributes of each service fault, and the structure of the method is as follows from top to bottom: the root node is an input service sample attribute value; one layer of nodes are service fault characteristic attributes; fluctuation range of the characteristic attribute of the two-layer node; the three layers of nodes are the specific service fault types; and the four-layer node service fault isolation degradation strategy.
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